Approach based on ICA and GCA to identify field mixed acoustic objects

With independent component analysis (ICA) to realize the blind separation from mixed acoustic objects, an identification method based on gray correlation analysis (GCA) is proposed through extracting linear prediction coefficient (LPC) feature. It is revealed that LPC is consistently better than wavelet energy feature, ICA is efficient algorithm to estimate the unknown signal level and GCA may reflect the difference and similarity of the influences of factors or characteristics effectively. The validity of the new methods is verified via examples in mixed acoustic objects identification system.